Skill Gap Analysis

Personal and Market-Based Assessment

Authors

Affiliation

Team 8

Jianhao Hong

Boston University

Xinran Li

Boston University

Chialing Sung

Boston University

Zimo Zeng

Boston University

1 🎯 Objective

This section compares the self-assessed technical skills of team members with the key competencies demanded in IT job postings, particularly within data and machine learning roles. The goal is to identify knowledge gaps and design personalized learning plans to enhance project readiness and team-wide growth.

import pandas as pd
import plotly.express as px

skills_data = {
    "Name": ["Jianhao Hong", "Xinran Li", "Chialing Sung", "Zimo Zeng"],
    "Python": [4, 2, 3, 2],
    "SQL": [5, 4, 2, 3],
    "Machine Learning": [2, 2, 3, 1],
    "Cloud Computing": [2, 3, 2, 4]
}

df_skills = pd.DataFrame(skills_data)
df_skills.set_index("Name", inplace=True)
df_skills

	Python	SQL	Machine Learning	Cloud Computing
Name
Jianhao Hong	4	5	2	2
Xinran Li	2	4	2	3
Chialing Sung	3	2	3	2
Zimo Zeng	2	3	1	4

import plotly.express as px

fig = px.imshow(
    df_skills,
    text_auto=True,
    color_continuous_scale="RdBu",
    aspect="auto",
    labels=dict(x="Skill", y="Team Member"),
    title="Team Skill Levels Heatmap",
    template="plotly_white"
)
fig.write_image("_output/skill_1_levels_heatmap.png")
fig.show()

top_skills = ["Python", "SQL", "Machine Learning", "Cloud Computing", "Docker", "AWS"]

for skill in top_skills:
    if skill not in df_skills.columns:
        df_skills[skill] = 0

fig = px.imshow(
    df_skills[top_skills],
    text_auto=True,
    color_continuous_scale="RdBu",
    aspect="auto",
    labels=dict(x="Skill", y="Team Member"),
    title="Team Skill Levels vs Industry Demands",
    template="plotly_white"
)
fig.write_image("_output/skill_2_team_level_vs_industry_demands.png")
fig.show()

2 Improvement Plan: Addressing the Skill Gap

2.1 Skill Gap Summary

From the heatmap comparison between team skills and industry-demanded skills (e.g., Python, SQL, Machine Learning, Cloud Computing, Docker, AWS), the following insights emerge:

• All team members lack Docker and AWS skills, which are widely required in modern DevOps and cloud roles.
• Jianhao Hong demonstrates strong SQL and Python skills, but could improve Cloud Computing and gain introductory experience in Docker and AWS.
• Xinran Li shows good balance, especially in Cloud Computing, but should focus on deeper Machine Learning knowledge and Docker basics.
• Chialing Sung has solid Machine Learning capability and moderate Python, but needs exposure to cloud platforms and DevOps tools.
• Zimo Zeng excels in Cloud Computing but has a significant gap in Machine Learning and scripting languages like Python.

2.2 Recommended Resources

Skill	Suggested Resource
Docker	Docker 101 Training
AWS	AWS Skill Builder
Python	Codecademy – Learn Python 3
Machine Learning	Coursera – Stanford ML
Cloud Computing	Google Cloud Skills Boost

2.3 Team Development Suggestions

• Establish peer-led workshops where stronger members (e.g., Jianhao in SQL) teach others.
• Assign self-learning goals based on individual gaps (e.g., Docker for all).
• Track weekly progress and share reflections in a team Slack channel or Notion board.